The Women’s Health Initiative, Cohort Studies, and the Population Science Research Agenda

471 Views

The Women’s Health Initiative, Cohort Studies, and the Population Science Research Agenda How can we obtain answers concerning health benefits and risks of behavior changes (interventions), and know that the answers are reliable?

The Women’s Health Initiative, Cohort Studies, and the Population Science Research Agenda

Presentation Transcript

The Women’s Health Initiative, Cohort Studies, and the Population Science Research Agenda • How can we obtain answers concerning health benefits and risks of behavior changes (interventions), and know that the answers are reliable? • Major research tools each have important limitations (RCT; intermediate outcome trial; cohort and case-control studies) • Most population science research is outcome-centric, rather than intervention-centric. • Suitable forums for identifying priority research opportunities and needed methodology development are generally lacking. Ross L. Prentice Fred Hutchinson Cancer Research Center and University of Washington

Major Research Tools Each Have Important Limitations Randomized controlled intervention trials • Cost, logistics, intervention adherence? • Only a small number are feasible at any time. Intermediate outcome clinical trials • Sufficiently comprehensive outcomes? • Methods to integrate data across many short-term outcomes? • Ability to replace full-scale clinical outcome trial? (surrogate outcomes) Observational studies • When are potential biases negligible? (confounding, selection, measurement error) • What assurance can be provided by replication in multiple populations? • How does reliability depend on nature of exposure variable/potential intervention and its measurement characteristics?

Some Possible Ways Forward Comparative and joint analysis of RCT and Observational Study data • Differences may reflect residual bias in observational study (or differences in study populations; limitations of data analysis procedures; study power or adherence issues, or differential outcome ascertainment in either study type). • Joint analyses may usefully extend RCT results. Enhanced role of biomarkers to strengthen each type of study • Biomarkers to calibrate difficult-to-measure exposures in observational studies, and for explanatory analysis of intervention effects on RCTs. • Biomarkers to enhance comprehensiveness of intermediate outcome RCTs. Cooperative group to advise NIH and other funding sources on research opportunities and needs in chronic disease population research

Distribution of Women in the WHI Hormone Therapy Clinical Trials (CT), and in Corresponding Observational Study (OS) Subcohorts, According to Prior Use of Postmenopausal Hormone Therapy (HT) and Gap Time from Menopause to First Use of HT, Among Hormone Therapy Users *Prior HT is defined relative to WHI enrollment in the CT and in the non-user groups in the OS. Prior HT in the user groups in the OS is defined relative to the beginning of the on-going HT episode at enrollment.

Breast Cancer Hazard Ratio Estimates according to Prior Postmenopausal Hormone Therapy Status, Years from Hormone Therapy Initiation, and Gap Time from Menopause to Hormone Therapy Initiation, among Women Adhering to their Baseline Hormone Therapy Status *Gap time in years from menopause to first use of HT

Comparison of Cancer Incidence Rates between Intervention and Comparison Groups in the Women’s Health Initiative (WHI) Dietary Modification Trial* *Trial includes 19,541 women in the intervention group and 29,294 women in the comparison group. †Weighted log-rank test (two-sided) stratified by age (5-year categories) and randomization status in the WHI hormone therapy trial. Weights increase linearly from zero at random assignment to a maximum of 1.0 at 10 years. ‡HR= hazard ratio; CI =confidence interval, from a proportional hazards model stratified by age (5-year categories), and randomization status in the WHI hormone therapy trial.

Nature and magnitude of random and systematic bias likely • varies among assessment instruments. • Systematic bias may relate to many factors (e.g., age, • ethnicity, body mass, behavioral factors). • Bingham et al (2003, Lancet) report a positive association between breast cancer and total and fat when consumption was assessed using a 7-day food diary, but the association was modest and non-significant when consumption was assessed with a FFQ. Very similar results from 4-day food record and FFQ analyses among DM comparison group women (Freedman et al 2006, IJE). • Objective measures (biomarkers) are needed to make progress in this important research area. Biomarker assessments in substudies (such as DLW measures of total energy expenditure) can be used to calibrate self-report assessments.